Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations264
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.5 KiB
Average record size in memory83.5 B

Variable types

Categorical2
Numeric10

Alerts

AGE is highly overall correlated with BMI and 2 other fieldsHigh correlation
BMI is highly overall correlated with AGE and 2 other fieldsHigh correlation
Class is highly overall correlated with AGE and 2 other fieldsHigh correlation
Cr is highly overall correlated with UreaHigh correlation
HbA1c is highly overall correlated with AGE and 2 other fieldsHigh correlation
TG is highly overall correlated with VLDLHigh correlation
Urea is highly overall correlated with CrHigh correlation
VLDL is highly overall correlated with TGHigh correlation

Reproduction

Analysis started2025-10-20 14:15:09.088440
Analysis finished2025-10-20 14:15:26.727470
Duration17.64 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size520.0 B
1
144 
0
120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1144
54.5%
0120
45.5%

Length

2025-10-20T14:15:26.828404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T14:15:26.903566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1144
54.5%
0120
45.5%

Most occurring characters

ValueCountFrequency (%)
1144
54.5%
0120
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1144
54.5%
0120
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1144
54.5%
0120
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1144
54.5%
0120
45.5%

AGE
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.522727
Minimum25
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:27.009850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile31
Q143
median50
Q355.25
95-th percentile64
Maximum77
Range52
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation10.127301
Coefficient of variation (CV)0.20449804
Kurtosis-0.19573344
Mean49.522727
Median Absolute Deviation (MAD)6
Skewness-0.28039397
Sum13074
Variance102.56222
MonotonicityNot monotonic
2025-10-20T14:15:27.134073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5529
 
11.0%
5027
 
10.2%
5417
 
6.4%
4915
 
5.7%
6013
 
4.9%
3010
 
3.8%
5710
 
3.8%
3310
 
3.8%
449
 
3.4%
408
 
3.0%
Other values (32)116
43.9%
ValueCountFrequency (%)
251
 
0.4%
262
 
0.8%
3010
3.8%
314
 
1.5%
321
 
0.4%
3310
3.8%
342
 
0.8%
357
2.7%
363
 
1.1%
382
 
0.8%
ValueCountFrequency (%)
772
 
0.8%
731
 
0.4%
694
1.5%
683
1.1%
671
 
0.4%
652
 
0.8%
642
 
0.8%
633
1.1%
621
 
0.4%
615
1.9%

Urea
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6715152
Minimum1.1
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:27.263636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.1
Q13.6
median4.7
Q36.1
95-th percentile12.87
Maximum26.4
Range25.3
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.0028367
Coefficient of variation (CV)0.70577908
Kurtosis10.187269
Mean5.6715152
Median Absolute Deviation (MAD)1.3
Skewness2.9688506
Sum1497.28
Variance16.022702
MonotonicityNot monotonic
2025-10-20T14:15:27.390646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514
 
5.3%
4.712
 
4.5%
410
 
3.8%
3.39
 
3.4%
39
 
3.4%
4.88
 
3.0%
4.68
 
3.0%
4.47
 
2.7%
7.56
 
2.3%
3.86
 
2.3%
Other values (73)175
66.3%
ValueCountFrequency (%)
1.11
 
0.4%
1.21
 
0.4%
1.82
 
0.8%
1.91
 
0.4%
25
1.9%
2.15
1.9%
2.22
 
0.8%
2.33
1.1%
2.41
 
0.4%
2.52
 
0.8%
ValueCountFrequency (%)
26.41
 
0.4%
243
1.1%
222
0.8%
20.83
1.1%
17.11
 
0.4%
14.91
 
0.4%
14.51
 
0.4%
13.51
 
0.4%
13.21
 
0.4%
111
 
0.4%

Cr
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.806818
Minimum6
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:27.515833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28
Q146
median61
Q382.25
95-th percentile224.25
Maximum800
Range794
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation99.400047
Coefficient of variation (CV)1.1584167
Kurtosis30.308609
Mean85.806818
Median Absolute Deviation (MAD)17
Skewness5.0092364
Sum22653
Variance9880.3694
MonotonicityNot monotonic
2025-10-20T14:15:27.651964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5512
 
4.5%
5310
 
3.8%
709
 
3.4%
1068
 
3.0%
747
 
2.7%
397
 
2.7%
546
 
2.3%
446
 
2.3%
386
 
2.3%
676
 
2.3%
Other values (80)187
70.8%
ValueCountFrequency (%)
61
 
0.4%
202
0.8%
221
 
0.4%
232
0.8%
242
0.8%
251
 
0.4%
261
 
0.4%
271
 
0.4%
284
1.5%
303
1.1%
ValueCountFrequency (%)
8003
1.1%
4013
1.1%
3702
0.8%
3441
 
0.4%
3271
 
0.4%
3151
 
0.4%
2431
 
0.4%
2301
 
0.4%
2281
 
0.4%
2031
 
0.4%

HbA1c
Real number (ℝ)

High correlation 

Distinct74
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8627273
Minimum0.9
Maximum14.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:27.784643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile4
Q15
median6.1
Q38.2
95-th percentile11.985
Maximum14.6
Range13.7
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.5446035
Coefficient of variation (CV)0.37078604
Kurtosis0.11707699
Mean6.8627273
Median Absolute Deviation (MAD)1.5
Skewness0.74100213
Sum1811.76
Variance6.475007
MonotonicityNot monotonic
2025-10-20T14:15:27.912090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
422
 
8.3%
516
 
6.1%
5.415
 
5.7%
614
 
5.3%
4.38
 
3.0%
98
 
3.0%
78
 
3.0%
4.97
 
2.7%
4.16
 
2.3%
6.86
 
2.3%
Other values (64)154
58.3%
ValueCountFrequency (%)
0.93
 
1.1%
3.72
 
0.8%
422
8.3%
4.16
 
2.3%
4.24
 
1.5%
4.38
 
3.0%
4.54
 
1.5%
4.71
 
0.4%
4.81
 
0.4%
4.97
 
2.7%
ValueCountFrequency (%)
14.61
 
0.4%
13.81
 
0.4%
13.71
 
0.4%
13.21
 
0.4%
12.91
 
0.4%
12.42
0.8%
12.34
1.5%
12.22
0.8%
121
 
0.4%
11.92
0.8%

Chol
Real number (ℝ)

Distinct57
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5943939
Minimum0
Maximum9.5
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:28.322092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9
Q13.875
median4.5
Q35.3
95-th percentile6.6
Maximum9.5
Range9.5
Interquartile range (IQR)1.425

Descriptive statistics

Standard deviation1.2890621
Coefficient of variation (CV)0.28057282
Kurtosis2.1710583
Mean4.5943939
Median Absolute Deviation (MAD)0.7
Skewness0.50282492
Sum1212.92
Variance1.661681
MonotonicityNot monotonic
2025-10-20T14:15:28.450487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
418
 
6.8%
4.213
 
4.9%
4.913
 
4.9%
4.312
 
4.5%
4.810
 
3.8%
4.710
 
3.8%
3.410
 
3.8%
4.69
 
3.4%
4.49
 
3.4%
3.78
 
3.0%
Other values (47)152
57.6%
ValueCountFrequency (%)
01
 
0.4%
0.51
 
0.4%
25
1.9%
2.11
 
0.4%
2.32
 
0.8%
2.41
 
0.4%
2.71
 
0.4%
2.81
 
0.4%
2.93
1.1%
33
1.1%
ValueCountFrequency (%)
9.51
0.4%
9.21
0.4%
9.11
0.4%
8.81
0.4%
81
0.4%
7.61
0.4%
7.51
0.4%
7.31
0.4%
7.21
0.4%
6.91
0.4%

TG
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1518939
Minimum0.6
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:28.569504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.7
Q11.3
median1.8
Q32.725
95-th percentile4.585
Maximum8.7
Range8.1
Interquartile range (IQR)1.425

Descriptive statistics

Standard deviation1.2658411
Coefficient of variation (CV)0.58824514
Kurtosis2.9667458
Mean2.1518939
Median Absolute Deviation (MAD)0.6
Skewness1.5321763
Sum568.1
Variance1.6023538
MonotonicityNot monotonic
2025-10-20T14:15:28.702582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.320
 
7.6%
1.816
 
6.1%
216
 
6.1%
1.914
 
5.3%
1.614
 
5.3%
1.713
 
4.9%
1.511
 
4.2%
2.911
 
4.2%
1.111
 
4.2%
1.410
 
3.8%
Other values (35)128
48.5%
ValueCountFrequency (%)
0.66
 
2.3%
0.79
3.4%
0.810
3.8%
0.95
 
1.9%
15
 
1.9%
1.111
4.2%
1.210
3.8%
1.320
7.6%
1.410
3.8%
1.511
4.2%
ValueCountFrequency (%)
8.71
 
0.4%
62
 
0.8%
5.92
 
0.8%
5.35
1.9%
5.11
 
0.4%
4.91
 
0.4%
4.81
 
0.4%
4.61
 
0.4%
4.54
1.5%
4.43
1.1%

HDL
Real number (ℝ)

Distinct25
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1828788
Minimum0.4
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:28.816666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.7
Q10.9
median1.1
Q31.325
95-th percentile2.085
Maximum4
Range3.6
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.45559059
Coefficient of variation (CV)0.38515408
Kurtosis5.8350725
Mean1.1828788
Median Absolute Deviation (MAD)0.2
Skewness1.7370948
Sum312.28
Variance0.20756278
MonotonicityNot monotonic
2025-10-20T14:15:28.918407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.938
14.4%
131
11.7%
1.328
10.6%
1.125
9.5%
0.824
9.1%
1.223
8.7%
1.419
7.2%
0.713
 
4.9%
1.79
 
3.4%
1.68
 
3.0%
Other values (15)46
17.4%
ValueCountFrequency (%)
0.44
 
1.5%
0.52
 
0.8%
0.67
 
2.7%
0.713
 
4.9%
0.752
 
0.8%
0.824
9.1%
0.938
14.4%
131
11.7%
1.031
 
0.4%
1.125
9.5%
ValueCountFrequency (%)
41
 
0.4%
2.54
1.5%
2.44
1.5%
2.33
 
1.1%
2.12
 
0.8%
22
 
0.8%
1.96
2.3%
1.84
1.5%
1.79
3.4%
1.68
3.0%

LDL
Real number (ℝ)

Distinct45
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5308712
Minimum0.3
Maximum5.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:29.032966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q11.8
median2.5
Q33.2
95-th percentile4.2
Maximum5.6
Range5.3
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0001728
Coefficient of variation (CV)0.39518913
Kurtosis-0.39363532
Mean2.5308712
Median Absolute Deviation (MAD)0.7
Skewness0.32101409
Sum668.15
Variance1.0003456
MonotonicityNot monotonic
2025-10-20T14:15:29.177960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
317
 
6.4%
2.614
 
5.3%
3.613
 
4.9%
1.813
 
4.9%
1.412
 
4.5%
2.512
 
4.5%
211
 
4.2%
1.311
 
4.2%
2.49
 
3.4%
2.89
 
3.4%
Other values (35)143
54.2%
ValueCountFrequency (%)
0.31
 
0.4%
0.751
 
0.4%
0.81
 
0.4%
0.95
1.9%
0.952
 
0.8%
15
1.9%
1.14
 
1.5%
1.23
 
1.1%
1.311
4.2%
1.352
 
0.8%
ValueCountFrequency (%)
5.61
 
0.4%
5.51
 
0.4%
4.92
 
0.8%
4.81
 
0.4%
4.61
 
0.4%
4.35
1.9%
4.25
1.9%
4.16
2.3%
42
 
0.8%
3.92
 
0.8%

VLDL
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4791667
Minimum0.2
Maximum31.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:29.291373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.4
Q10.675
median0.9
Q31.3
95-th percentile2.2
Maximum31.8
Range31.6
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation3.0998561
Coefficient of variation (CV)2.0956774
Kurtosis54.817911
Mean1.4791667
Median Absolute Deviation (MAD)0.3
Skewness7.0042464
Sum390.5
Variance9.609108
MonotonicityNot monotonic
2025-10-20T14:15:29.406553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.733
12.5%
0.830
11.4%
0.625
 
9.5%
0.921
 
8.0%
1.319
 
7.2%
0.516
 
6.1%
116
 
6.1%
1.115
 
5.7%
0.413
 
4.9%
0.310
 
3.8%
Other values (23)66
25.0%
ValueCountFrequency (%)
0.22
 
0.8%
0.310
 
3.8%
0.413
 
4.9%
0.516
6.1%
0.625
9.5%
0.733
12.5%
0.830
11.4%
0.921
8.0%
116
6.1%
1.115
5.7%
ValueCountFrequency (%)
31.81
0.4%
24.51
0.4%
22.21
0.4%
14.51
0.4%
13.11
0.4%
11.31
0.4%
101
0.4%
71
0.4%
51
0.4%
4.11
0.4%

BMI
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.626856
Minimum19
Maximum43.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-10-20T14:15:29.520835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q123
median25
Q330
95-th percentile36.5265
Maximum43.25
Range24.25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0936518
Coefficient of variation (CV)0.19129753
Kurtosis-0.33317551
Mean26.626856
Median Absolute Deviation (MAD)4
Skewness0.68804824
Sum7029.49
Variance25.945289
MonotonicityNot monotonic
2025-10-20T14:15:29.638018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2439
14.8%
2129
 
11.0%
2221
 
8.0%
2320
 
7.6%
3319
 
7.2%
3015
 
5.7%
2915
 
5.7%
2713
 
4.9%
2610
 
3.8%
2510
 
3.8%
Other values (28)73
27.7%
ValueCountFrequency (%)
195
 
1.9%
19.51
 
0.4%
207
 
2.7%
2129
11.0%
21.171
 
0.4%
2221
8.0%
22.51
 
0.4%
2320
7.6%
23.51
 
0.4%
2439
14.8%
ValueCountFrequency (%)
43.251
 
0.4%
395
1.9%
381
 
0.4%
375
1.9%
36.62
 
0.8%
36.111
 
0.4%
362
 
0.8%
354
1.5%
344
1.5%
33.91
 
0.4%

Class
Categorical

High correlation 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size528.0 B
2
128 
0
96 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters264
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Length

2025-10-20T14:15:29.744746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-20T14:15:29.810184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Most occurring characters

ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2128
48.5%
096
36.4%
140
 
15.2%

Interactions

2025-10-20T14:15:25.577375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:10.201174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:12.130871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:14.109135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:16.735361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:20.237962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.711090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.660725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.606663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.674415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.657564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:10.429592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:12.292134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:14.299750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:17.203618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:20.877105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.817891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.751414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.680216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.762239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.740840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:10.583985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:12.500289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:14.490437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:17.442902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:20.998531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.908245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.856461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.764458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.842963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.831767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:10.785991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:12.708757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:14.719290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:17.678563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.106354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.010545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.956329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.874403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.954947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.920444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:10.919736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.106517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:14.986368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:17.915058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.192721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.106608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.049184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.953196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.042863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:26.012600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:11.123497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.328137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:15.232385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:18.172029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.267757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.198648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.139945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.264580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.125674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:26.101133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:11.342463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.483778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:15.458979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:18.409980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.362563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.294059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.236448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.352406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.225523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:26.194209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:11.573571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.642747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:15.822194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:18.906875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.457347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.393157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.329286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.437145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.318550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:26.277332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:11.721904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.776555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:16.132705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:19.252499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.543662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.480798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.422273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.511273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.403091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:26.363843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:11.882121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:13.944083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:16.311917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:19.652846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:21.633436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:22.572852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:23.512433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:24.593534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-20T14:15:25.492897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-20T14:15:29.886238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGEBMICholClassCrGenderHDLHbA1cLDLTGUreaVLDL
AGE1.0000.6420.0660.5620.1960.134-0.0580.558-0.0470.2760.3110.235
BMI0.6421.0000.1670.5840.1720.1270.0200.706-0.0700.3270.2460.323
Chol0.0660.1671.0000.2160.0080.1090.0570.2240.4550.3230.1220.309
Class0.5620.5840.2161.0000.1210.2140.0000.7680.0690.2400.2160.000
Cr0.1960.1720.0080.1211.0000.121-0.0450.1270.0790.2150.6820.240
Gender0.1340.1270.1090.2140.1211.0000.1060.2100.0260.1150.2360.098
HDL-0.0580.0200.0570.000-0.0450.1061.000-0.013-0.125-0.105-0.049-0.048
HbA1c0.5580.7060.2240.7680.1270.210-0.0131.000-0.1230.3190.2370.310
LDL-0.047-0.0700.4550.0690.0790.026-0.125-0.1231.0000.1000.0730.096
TG0.2760.3270.3230.2400.2150.115-0.1050.3190.1001.0000.2040.640
Urea0.3110.2460.1220.2160.6820.236-0.0490.2370.0730.2041.0000.170
VLDL0.2350.3230.3090.0000.2400.098-0.0480.3100.0960.6400.1701.000

Missing values

2025-10-20T14:15:26.497642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-20T14:15:26.612204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMIClass
00504.7464.94.20.92.41.40.524.00
11264.5624.93.71.41.12.10.623.00
21337.1464.94.91.00.82.00.421.00
30452.3244.02.91.01.01.50.421.00
40502.0504.03.61.30.92.10.624.00
51484.7474.02.90.80.91.60.424.00
61432.6674.03.80.92.43.71.021.00
70323.6284.03.82.02.43.81.024.00
80314.4554.23.60.71.71.60.323.00
90333.3534.04.01.10.92.71.021.00
GenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMIClass
2541527.83157.73.51.31.01.61.433.02
25515314.932711.05.98.71.03.03.433.02
2561574.63706.84.06.02.53.51.137.02
2571574.63706.86.16.02.53.51.137.02
25805524.04016.34.32.90.42.71.328.02
25906124.04017.04.32.90.42.71.330.02
26006124.04017.04.32.90.42.71.336.62
26116020.88009.02.31.10.90.90.533.02
26215620.88009.04.62.01.22.50.935.02
26315820.88009.16.62.91.14.31.333.02